If there were aggregate effects, how would they show up in Spain and Shakeshaft? Just going by the abstract, Spain is looking for genes where the rare variant has a positive effect. That is the opposite of the mutational load theory, and they don’t find any. I think Shakeshaft reaches the same conclusion by pedigree analysis.
Say that there are 10k genes, MAF=0.01, each worth 1.5 IQ points. What would Spain detect? If the TIP population is 10/3σ,* then these 10k genes each appear as the mutant 2⁄3 as often, 20 hits, rather than the expected 30. That’s a 2 sigma event. So if an oracle gave you this list of 10k genes, you could use Spain to confirm it. But if you have to find the list, it’s harder. They should expect 5k false positives among the 200k variants that they tested. If all of the true genes were among the 200k, there would be 15k hits rather than the expected 5k, confirming the theory. But with poor coverage, the true hits might be lost in the noise. And even if they have good coverage, they have restricted to non-synonymous protein coding mutations.
Moreover that model is what Steve Hsu believes, not the mutational load hypothesis. Spain et al can’t test the mutational load hypothesis: if the relevant genes are rarer or have smaller effect, they wouldn’t notice them at all. On the other hand, if the TIP population really is 5σ, it would be possible to detect more.
* The TIP population is usually described as 0.03% of the population, which is 3.4σ under a normal distribution, but I chose 10⁄3 for simplicity of calculation. They score about 5σ in raw SAT. Self-selection probably means that they’re actually rarer than 0.03%, but probably not much.
If there were aggregate effects, how would they show up in Spain and Shakeshaft? Just going by the abstract, Spain is looking for genes where the rare variant has a positive effect. That is the opposite of the mutational load theory, and they don’t find any. I think Shakeshaft reaches the same conclusion by pedigree analysis.
Say that there are 10k genes, MAF=0.01, each worth 1.5 IQ points. What would Spain detect? If the TIP population is 10/3σ,* then these 10k genes each appear as the mutant 2⁄3 as often, 20 hits, rather than the expected 30. That’s a 2 sigma event. So if an oracle gave you this list of 10k genes, you could use Spain to confirm it. But if you have to find the list, it’s harder. They should expect 5k false positives among the 200k variants that they tested. If all of the true genes were among the 200k, there would be 15k hits rather than the expected 5k, confirming the theory. But with poor coverage, the true hits might be lost in the noise. And even if they have good coverage, they have restricted to non-synonymous protein coding mutations.
Moreover that model is what Steve Hsu believes, not the mutational load hypothesis. Spain et al can’t test the mutational load hypothesis: if the relevant genes are rarer or have smaller effect, they wouldn’t notice them at all. On the other hand, if the TIP population really is 5σ, it would be possible to detect more.
* The TIP population is usually described as 0.03% of the population, which is 3.4σ under a normal distribution, but I chose 10⁄3 for simplicity of calculation. They score about 5σ in raw SAT. Self-selection probably means that they’re actually rarer than 0.03%, but probably not much.